| Paper: | FR-A2.3 |
| Session: | Atmospheric Applications of Radiometry II |
| Time: | Friday, March 30, 11:20 - 11:40 |
| Presentation: |
Oral
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| Topic: |
Clouds and precipitation: |
| Title: |
Validating Enhanced Resolution of Microwave Sounder Imagery through Fusion with Infrared Sensor Data |
| Authors: |
Igor Yanovsky; NASA Jet Propulsion Laboratory | | |
| | Yixin Wen; NASA Jet Propulsion Laboratory | | |
| | Ali Behrangi; NASA Jet Propulsion Laboratory | | |
| | Mathias Schreier; NASA Jet Propulsion Laboratory | | |
| | Bjorn Lambrigtsen; NASA Jet Propulsion Laboratory | | |
| Abstract: |
Microwave sensors are able to penetrate through thick clouds to see the structure of a storm. The images collected are valuable for evaluating the storm's internal processes and its strength. However, the data, such as brightness temperatures, acquired by microwave sensors are blurry and of low-resolution, and thus all derived products, including rain rates will share that characteristic. On the other hand, the images obtained using infrared/visible sensors, and their corresponding products can offer higher resolution but with negligible ability to penetrate into clouds. In this paper, we develop and validate a data fusion methodology and apply it to enhance the resolution of a microwave image using the data from a collocated infrared/visible sensor. Such an approach takes advantage of the spatial resolution of the infrared instrument and the sensing accuracy of the microwave instrument.
The developed data fusion methodology leverages sparsity in signals and is based on current research in sparse optimization and compressed sensing. In our previous work on super-resolution, we considered the deconvolution inverse problem, where we deblurred images from the effects of a point spread function, and developed a simultaneous deconvolution and upsampling algorithm in order to enhance the effective spatial resolution of an image. Since the convolution problem in the presence of noise is highly ill-posed, regularization was applied to achieve stability while preserving a priori properties of the solution. We formulated the restoration problem within the variational framework, using the total variation regularization. Total variation of an image measures the sum of the absolute values of its gradient and increases in the presence of irregularities. By minimizing the total variation, we showed that the process significantly reduces the brightness temperature errors in the overall image. We performed the total variation-based deconvolution within the split Bregman optimization framework to achieve a significant computational time improvement over already robust total-variation gradient descent-based techniques. In this paper, we consider the simultaneous data fusion, deconvolution, and upsampling problem, where we not only enhance the effective resolution of a microwave image, but further enhance its resolution using the data from an infrared instrument.
We test our method using a precipitation scene captured with the Advanced Microwave Sounding Unit (AMSU-B) microwave instrument and the Advanced Very High Resolution Radiometer (AVHRR) infrared instrument and compare the results to simultaneous radar observations. We show that the data fusion product is better than the original AMSU-B and AVHRR observations across all statistical indicators.
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